




'''simple docstring'''







import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase=1						):






   '''simple docstring'''







   if n_shave_prefix_segments >= 0:
      return ".".join(path.split('.'						)[n_shave_prefix_segments:]						)
   else:
      return ".".join(path.split('.'						)[:n_shave_prefix_segments]						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase=0						):






   '''simple docstring'''







   A__       =     []
   for old_item in old_list:
      A__       =     old_item.replace('in_layers.0'      ,'norm1'						)
      A__       =     new_item.replace('in_layers.2'      ,'conv1'						)

      A__       =     new_item.replace('out_layers.0'      ,'norm2'						)
      A__       =     new_item.replace('out_layers.3'      ,'conv2'						)

      A__       =     new_item.replace('emb_layers.1'      ,'time_emb_proj'						)
      A__       =     new_item.replace('skip_connection'      ,'conv_shortcut'						)

      A__       =     shave_segments(UpperCAmelCase      ,n_shave_prefix_segments=UpperCAmelCase						)

      mapping.append({'old': old_item, 'new': new_item}						)

   return mapping







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase=0						):






   '''simple docstring'''







   A__       =     []
   for old_item in old_list:
      A__       =     old_item

      A__       =     new_item.replace('norm.weight'      ,'group_norm.weight'						)
      A__       =     new_item.replace('norm.bias'      ,'group_norm.bias'						)

      A__       =     new_item.replace('proj_out.weight'      ,'proj_attn.weight'						)
      A__       =     new_item.replace('proj_out.bias'      ,'proj_attn.bias'						)

      A__       =     shave_segments(UpperCAmelCase      ,n_shave_prefix_segments=UpperCAmelCase						)

      mapping.append({'old': old_item, 'new': new_item}						)

   return mapping







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=None      ,UpperCAmelCase=None      ,UpperCAmelCase=None						):






   '''simple docstring'''







   assert isinstance(UpperCAmelCase      ,UpperCAmelCase						), "Paths should be a list of dicts containing 'old' and 'new' keys."

   # Splits the attention layers into three variables.
   if attention_paths_to_split is not None:
      for path, path_map in attention_paths_to_split.items():
         A__       =     old_checkpoint[path]
         A__       =     old_tensor.shape[0] // 3

         A__       =     (-1, channels) if len(old_tensor.shape						) == 3 else (-1)

         A__       =     old_tensor.shape[0] // config['num_head_channels'] // 3

         A__       =     old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]						)
         A__  ,			A__  ,			A__       =     old_tensor.split(channels // num_heads      ,dim=1						)

         A__       =     query.reshape(UpperCAmelCase						)
         A__       =     key.reshape(UpperCAmelCase						)
         A__       =     value.reshape(UpperCAmelCase						)

   for path in paths:
      A__       =     path['new']

      # These have already been assigned
      if attention_paths_to_split is not None and new_path in attention_paths_to_split:
         continue

      # Global renaming happens here
      A__       =     new_path.replace('middle_block.0'      ,'mid_block.resnets.0'						)
      A__       =     new_path.replace('middle_block.1'      ,'mid_block.attentions.0'						)
      A__       =     new_path.replace('middle_block.2'      ,'mid_block.resnets.1'						)

      if additional_replacements is not None:
         for replacement in additional_replacements:
            A__       =     new_path.replace(replacement['old']      ,replacement['new']						)

        # proj_attn.weight has to be converted from conv 1D to linear
      if "proj_attn.weight" in new_path:
         A__       =     old_checkpoint[path['old']][:, :, 0]
      else:
         A__       =     old_checkpoint[path['old']]







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     {}

   A__       =     checkpoint['time_embed.0.weight']
   A__       =     checkpoint['time_embed.0.bias']
   A__       =     checkpoint['time_embed.2.weight']
   A__       =     checkpoint['time_embed.2.bias']

   A__       =     checkpoint['input_blocks.0.0.weight']
   A__       =     checkpoint['input_blocks.0.0.bias']

   A__       =     checkpoint['out.0.weight']
   A__       =     checkpoint['out.0.bias']
   A__       =     checkpoint['out.2.weight']
   A__       =     checkpoint['out.2.bias']

   # Retrieves the keys for the input blocks only
   A__       =     len({'.'.join(layer.split('.'						)[:2]						) for layer in checkpoint if 'input_blocks' in layer}						)
   A__       =     {
       layer_id: [key for key in checkpoint if F"""input_blocks.{layer_id}""" in key]
       for layer_id in range(UpperCAmelCase						)
   }

   # Retrieves the keys for the middle blocks only
   A__       =     len({'.'.join(layer.split('.'						)[:2]						) for layer in checkpoint if 'middle_block' in layer}						)
   A__       =     {
       layer_id: [key for key in checkpoint if F"""middle_block.{layer_id}""" in key]
       for layer_id in range(UpperCAmelCase						)
   }

   # Retrieves the keys for the output blocks only
   A__       =     len({'.'.join(layer.split('.'						)[:2]						) for layer in checkpoint if 'output_blocks' in layer}						)
   A__       =     {
       layer_id: [key for key in checkpoint if F"""output_blocks.{layer_id}""" in key]
       for layer_id in range(UpperCAmelCase						)
   }

   for i in range(1      ,UpperCAmelCase						):
      A__       =     (i - 1) // (config['num_res_blocks'] + 1)
      A__       =     (i - 1) % (config['num_res_blocks'] + 1)

      A__       =     [key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key]
      A__       =     [key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]

      if F"""input_blocks.{i}.0.op.weight""" in checkpoint:
         A__       =     checkpoint[
             F"""input_blocks.{i}.0.op.weight"""
         ]
         A__       =     checkpoint[
             F"""input_blocks.{i}.0.op.bias"""
         ]
         continue

      A__       =     renew_resnet_paths(UpperCAmelCase						)
      A__       =     {'old': F"""input_blocks.{i}.0""", 'new': F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
      A__       =     {'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
      assign_to_checkpoint(
          UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,additional_replacements=[meta_path, resnet_op]      ,config=UpperCAmelCase						)

      if len(UpperCAmelCase						):
         A__       =     renew_attention_paths(UpperCAmelCase						)
         A__       =     {
             'old': F"""input_blocks.{i}.1""",
             'new': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
         }
         A__       =     {
             F"""input_blocks.{i}.1.qkv.bias""": {
                 'key': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
                 'query': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
                 'value': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
             },
             F"""input_blocks.{i}.1.qkv.weight""": {
                 'key': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
                 'query': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
                 'value': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
             },
         }
         assign_to_checkpoint(
             UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,additional_replacements=[meta_path]      ,attention_paths_to_split=UpperCAmelCase      ,config=UpperCAmelCase      ,)

   A__       =     middle_blocks[0]
   A__       =     middle_blocks[1]
   A__       =     middle_blocks[2]

   A__       =     renew_resnet_paths(UpperCAmelCase						)
   assign_to_checkpoint(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,config=UpperCAmelCase						)

   A__       =     renew_resnet_paths(UpperCAmelCase						)
   assign_to_checkpoint(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,config=UpperCAmelCase						)

   A__       =     renew_attention_paths(UpperCAmelCase						)
   A__       =     {
       'middle_block.1.qkv.bias': {
           'key': 'mid_block.attentions.0.key.bias',
           'query': 'mid_block.attentions.0.query.bias',
           'value': 'mid_block.attentions.0.value.bias',
       },
       'middle_block.1.qkv.weight': {
           'key': 'mid_block.attentions.0.key.weight',
           'query': 'mid_block.attentions.0.query.weight',
           'value': 'mid_block.attentions.0.value.weight',
       },
   }
   assign_to_checkpoint(
       UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,attention_paths_to_split=UpperCAmelCase      ,config=UpperCAmelCase						)

   for i in range(UpperCAmelCase						):
      A__       =     i // (config['num_res_blocks'] + 1)
      A__       =     i % (config['num_res_blocks'] + 1)
      A__       =     [shave_segments(UpperCAmelCase      ,2						) for name in output_blocks[i]]
      A__       =     {}

      for layer in output_block_layers:
         A__  ,			A__       =     layer.split('.'						)[0], shave_segments(UpperCAmelCase      ,1						)
         if layer_id in output_block_list:
            output_block_list[layer_id].append(UpperCAmelCase						)
         else:
            A__       =     [layer_name]

      if len(UpperCAmelCase						) > 1:
         A__       =     [key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
         A__       =     [key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]

         A__       =     renew_resnet_paths(UpperCAmelCase						)
         A__       =     renew_resnet_paths(UpperCAmelCase						)

         A__       =     {'old': F"""output_blocks.{i}.0""", 'new': F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
         assign_to_checkpoint(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,additional_replacements=[meta_path]      ,config=UpperCAmelCase						)

         if ["conv.weight", "conv.bias"] in output_block_list.values():
            A__       =     list(output_block_list.values()						).index(['conv.weight', 'conv.bias']						)
            A__       =     checkpoint[
                F"""output_blocks.{i}.{index}.conv.weight"""
            ]
            A__       =     checkpoint[
                F"""output_blocks.{i}.{index}.conv.bias"""
            ]

            # Clear attentions as they have been attributed above.
            if len(UpperCAmelCase						) == 2:
               A__       =     []

         if len(UpperCAmelCase						):
            A__       =     renew_attention_paths(UpperCAmelCase						)
            A__       =     {
                'old': F"""output_blocks.{i}.1""",
                'new': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
            }
            A__       =     {
                F"""output_blocks.{i}.1.qkv.bias""": {
                    'key': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
                    'query': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
                    'value': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
                },
                F"""output_blocks.{i}.1.qkv.weight""": {
                    'key': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
                    'query': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
                    'value': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
                },
            }
            assign_to_checkpoint(
                UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,additional_replacements=[meta_path]      ,attention_paths_to_split=to_split if any('qkv' in key for key in attentions						) else None      ,config=UpperCAmelCase      ,)
      else:
         A__       =     renew_resnet_paths(UpperCAmelCase      ,n_shave_prefix_segments=1						)
         for path in resnet_0_paths:
            A__       =     '.'.join(['output_blocks', str(UpperCAmelCase						), path['old']]						)
            A__       =     '.'.join(['up_blocks', str(UpperCAmelCase						), 'resnets', str(UpperCAmelCase						), path['new']]						)

            A__       =     checkpoint[old_path]

   return new_checkpoint


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser()

    parser.add_argument(
        '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
    )

    parser.add_argument(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help='''The config json file corresponding to the architecture.''',
    )

    parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

    lowerCAmelCase_     						=				parser.parse_args()

    lowerCAmelCase_     						=				torch.load(args.checkpoint_path)

    with open(args.config_file) as f:
        lowerCAmelCase_     						=				json.loads(f.read())

    lowerCAmelCase_     						=				convert_ldm_checkpoint(checkpoint, config)

    if "ldm" in config:
        del config["ldm"]

    lowerCAmelCase_     						=				UNetaDModel(**config)
    model.load_state_dict(converted_checkpoint)

    try:
        lowerCAmelCase_     						=				DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
        lowerCAmelCase_     						=				VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

        lowerCAmelCase_     						=				LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
        pipe.save_pretrained(args.dump_path)
    except:  # noqa: E722
        model.save_pretrained(args.dump_path)








'''simple docstring'''







import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_     						=				logging.get_logger(__name__)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase=False						):






   '''simple docstring'''







   A__       =     []
   for i in range(config.num_hidden_layers						):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""deit.encoder.layer.{i}.layernorm_before.weight""")						)
      rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""deit.encoder.layer.{i}.layernorm_before.bias""")						)
      rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""deit.encoder.layer.{i}.attention.output.dense.weight""")						)
      rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""deit.encoder.layer.{i}.attention.output.dense.bias""")						)
      rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""deit.encoder.layer.{i}.layernorm_after.weight""")						)
      rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""deit.encoder.layer.{i}.layernorm_after.bias""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""deit.encoder.layer.{i}.intermediate.dense.weight""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""deit.encoder.layer.{i}.intermediate.dense.bias""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""deit.encoder.layer.{i}.output.dense.weight""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""deit.encoder.layer.{i}.output.dense.bias""")						)

   # projection layer + position embeddings
   rename_keys.extend(
       [
           ('cls_token', 'deit.embeddings.cls_token'),
           ('dist_token', 'deit.embeddings.distillation_token'),
           ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
           ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
           ('pos_embed', 'deit.embeddings.position_embeddings'),
       ]						)

   if base_model:
      # layernorm + pooler
      rename_keys.extend(
          [
              ('norm.weight', 'layernorm.weight'),
              ('norm.bias', 'layernorm.bias'),
              ('pre_logits.fc.weight', 'pooler.dense.weight'),
              ('pre_logits.fc.bias', 'pooler.dense.bias'),
          ]						)

      # if just the base model, we should remove "deit" from all keys that start with "deit"
      A__       =     [(pair[0], pair[1][4:]) if pair[1].startswith('deit'						) else pair for pair in rename_keys]
   else:
      # layernorm + classification heads
      rename_keys.extend(
          [
              ('norm.weight', 'deit.layernorm.weight'),
              ('norm.bias', 'deit.layernorm.bias'),
              ('head.weight', 'cls_classifier.weight'),
              ('head.bias', 'cls_classifier.bias'),
              ('head_dist.weight', 'distillation_classifier.weight'),
              ('head_dist.bias', 'distillation_classifier.bias'),
          ]						)

   return rename_keys







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=False						):






   '''simple docstring'''







   for i in range(config.num_hidden_layers						):
      if base_model:
         A__       =     ''
      else:
         A__       =     'deit.'
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      A__       =     state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""						)
      A__       =     state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""						)
      # next, add query, keys and values (in that order) to the state dict
      A__       =     in_proj_weight[
          : config.hidden_size, :
      ]
      A__       =     in_proj_bias[: config.hidden_size]
      A__       =     in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      A__       =     in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      A__       =     in_proj_weight[
          -config.hidden_size :, :
      ]
      A__       =     in_proj_bias[-config.hidden_size :]







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     dct.pop(UpperCAmelCase						)
   A__       =     val







def 						_A      ( ):






   '''simple docstring'''







   A__       =     'http://images.cocodataset.org/val2017/000000039769.jpg'
   A__       =     Image.open(requests.get(UpperCAmelCase      ,stream=UpperCAmelCase						).raw						)
   return im







@torch.no_grad()
def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     DeiTConfig()
   # all deit models have fine-tuned heads
   A__       =     False
   # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
   A__       =     1000
   A__       =     'huggingface/label-files'
   A__       =     'imagenet-1k-id2label.json'
   A__       =     json.load(open(hf_hub_download(UpperCAmelCase      ,UpperCAmelCase      ,repo_type='dataset'						)      ,'r'						)						)
   A__       =     {int(UpperCAmelCase						): v for k, v in idalabel.items()}
   A__       =     idalabel
   A__       =     {v: k for k, v in idalabel.items()}
   A__       =     int(deit_name[-6:-4]						)
   A__       =     int(deit_name[-3:]						)
   # size of the architecture
   if deit_name[9:].startswith('tiny'						):
      A__       =     192
      A__       =     768
      A__       =     12
      A__       =     3
   elif deit_name[9:].startswith('small'						):
      A__       =     384
      A__       =     1536
      A__       =     12
      A__       =     6
   if deit_name[9:].startswith('base'						):
      pass
   elif deit_name[4:].startswith('large'						):
      A__       =     1024
      A__       =     4096
      A__       =     24
      A__       =     16

   # load original model from timm
   A__       =     timm.create_model(UpperCAmelCase      ,pretrained=UpperCAmelCase						)
   timm_model.eval()

   # load state_dict of original model, remove and rename some keys
   A__       =     timm_model.state_dict()
   A__       =     create_rename_keys(UpperCAmelCase      ,UpperCAmelCase						)
   for src, dest in rename_keys:
      rename_key(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)
   read_in_q_k_v(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)

   # load HuggingFace model
   A__       =     DeiTForImageClassificationWithTeacher(UpperCAmelCase						).eval()
   model.load_state_dict(UpperCAmelCase						)

   # Check outputs on an image, prepared by DeiTImageProcessor
   A__       =     int(
       (256 / 224) * config.image_size						)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
   A__       =     DeiTImageProcessor(size=UpperCAmelCase      ,crop_size=config.image_size						)
   A__       =     image_processor(images=prepare_img()      ,return_tensors='pt'						)
   A__       =     encoding['pixel_values']
   A__       =     model(UpperCAmelCase						)

   A__       =     timm_model(UpperCAmelCase						)
   assert timm_logits.shape == outputs.logits.shape
   assert torch.allclose(UpperCAmelCase      ,outputs.logits      ,atol=1e-3						)

   Path(UpperCAmelCase						).mkdir(exist_ok=UpperCAmelCase						)
   print(F"""Saving model {deit_name} to {pytorch_dump_folder_path}"""						)
   model.save_pretrained(UpperCAmelCase						)
   print(F"""Saving image processor to {pytorch_dump_folder_path}"""						)
   image_processor.save_pretrained(UpperCAmelCase						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--deit_name''',
        default='''vit_deit_base_distilled_patch16_224''',
        type=str,
        help='''Name of the DeiT timm model you\'d like to convert.''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
    )

    lowerCAmelCase_     						=				parser.parse_args()
    convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)


